Data Scientist (MLOps focus)

Company DescriptionAnderson Lane is an AI-native global macro investment firm that integrates modern AI with traditional investment strategies to drive innovative solutions. Our approach leverages structured financial, economic, and geopolitical data to generate insights across rates, FX, equities, and commodities, enabling risk-managed and cost-efficient trading. We aim to redefine how hedge funds operate by transitioning to AI-driven workflows that allow continuous learning and decision-making processes across markets. With a focus on global macro, we address the complexities of modern geopolitics, supply chain dynamics, and monetary experimentation in a fragmented financial landscape.Role DescriptionWe are seeking a Data Scientist with an MLOps focus for a full-time hybrid role based in the Austin, Texas Metropolitan Area, with the flexibility to work from home for part of the week. The Data Scientist will be responsible for designing, building, and maintaining machine learning pipelines (medallion architecture), implementing data-driven solutions, and optimizing models for scalability and deployment. The role also involves collaboration with cross-functional teams to analyze financial and economic datasets, develop predictive and probabilistic models, and create data visualizations to inform investment decisions.QualificationsProficiency in Data Science, including designing and developing machine learning models and pipelinesStrong foundation in Statistics and Data Analysis for analyzing complex structured and unstructured datasetsAt 5 years work experience in Data Science, Data Analysis, Machine Learning, or related filedExperience with Data Analytics and Data Visualization tools to derive actionable insights and present them effectivelyProficiency in programming languages including Python with other languages such as C++, R, SQL, or similar a plusHands-on experience with tools and frameworks like TensorFlow, PyTorch, as well as Snowflake, Redis, Postgres etc is a plusAbility to work effectively in a hybrid environment, including independent remote tasks and collaborative workExperience in financial markets or investment analytics is highly desirableMaster's or PhD in Data Science, Computer Science, Statistics, or a related field is preferred, not required if strong relevant experience